import os import json import matplotlib.pyplot as plt import numpy as np from pose_classifier import PoseClassifier import torch from collections import defaultdict def analyze_turning_patterns_detailed(dataset_path, num_samples=50): """详细分析转弯模式，基于相对于reference的pose变化""" classifier = PoseClassifier() samples_path = os.path.join(dataset_path, "samples") all_analyses = [] sample_count = 0 # 用于统计每个类别的样本 class_samples = defaultdict(list) print("=== 开始分析样本（基于相对于reference的变化）===") for item in sorted(os.listdir(samples_path)): # 排序以便有序输出 if sample_count >= num_samples: break sample_dir = os.path.join(samples_path, item) if os.path.isdir(sample_dir): poses_path = os.path.join(sample_dir, "poses.json") if os.path.exists(poses_path): try: with open(poses_path, 'r') as f: poses_data = json.load(f) target_relative_poses = poses_data['target_relative_poses'] if len(target_relative_poses) > 0: # 🔧 创建相对pose向量（已经是相对于reference的） pose_vecs = [] for pose_data in target_relative_poses: # 相对位移（已经是相对于reference计算的） translation = torch.tensor(pose_data['relative_translation'], dtype=torch.float32) # 🔧 相对旋转（需要从current和reference计算） current_rotation = torch.tensor(pose_data['current_rotation'], dtype=torch.float32) reference_rotation = torch.tensor(pose_data['reference_rotation'], dtype=torch.float32) # 计算相对旋转：q_relative = q_ref^-1 * q_current relative_rotation = calculate_relative_rotation(current_rotation, reference_rotation) # 组合为7D向量：[relative_translation, relative_rotation] pose_vec = torch.cat([translation, relative_rotation], dim=0) pose_vecs.append(pose_vec) if pose_vecs: pose_sequence = torch.stack(pose_vecs, dim=0) # 🔧 使用新的分析方法 analysis = classifier.analyze_pose_sequence(pose_sequence) analysis['sample_name'] = item all_analyses.append(analysis) # 🔧 详细输出每个样本的分类信息 print(f"\n--- 样本 {sample_count + 1}: {item} ---") print(f"总帧数: {analysis['total_frames']}") print(f"总距离: {analysis['total_distance']:.4f}") # 分类分布 class_dist = analysis['class_distribution'] print(f"分类分布:") for class_name, count in class_dist.items(): percentage = count / analysis['total_frames'] * 100 print(f" {class_name}: {count} 帧 ({percentage:.1f}%)") # 🔧 调试前几个pose的分类过程 print(f"前3帧的详细分类过程:") for i in range(min(3, len(pose_vecs))): debug_info = classifier.debug_single_pose( pose_vecs[i][:3], pose_vecs[i][3:7] ) print(f" 帧{i}: {debug_info['classification']} " f"(yaw: {debug_info['yaw_angle_deg']:.2f}°, " f"forward: {debug_info['forward_movement']:.3f})") # 运动段落 print(f"运动段落:") for i, segment in enumerate(analysis['motion_segments']): print(f" 段落{i+1}: {segment['class']} (帧 {segment['start_frame']}-{segment['end_frame']}, 持续 {segment['duration']} 帧)") # 🔧 确定主要运动类型 dominant_class = max(class_dist.items(), key=lambda x: x[1]) dominant_class_name = dominant_class[0] dominant_percentage = dominant_class[1] / analysis['total_frames'] * 100 print(f"主要运动类型: {dominant_class_name} ({dominant_percentage:.1f}%)") # 将样本添加到对应类别 class_samples[dominant_class_name].append({ 'name': item, 'percentage': dominant_percentage, 'analysis': analysis }) sample_count += 1 except Exception as e: print(f"❌ 处理样本 {item} 时出错: {e}") print("\n" + "="*60) print("=== 按类别分组的样本统计（基于相对于reference的变化）===") # 🔧 按类别输出样本列表 for class_name in ['forward', 'backward', 'left_turn', 'right_turn']: samples = class_samples[class_name] print(f"\n🔸 {class_name.upper()} 类样本 (共 {len(samples)} 个):") if samples: # 按主要类别占比排序 samples.sort(key=lambda x: x['percentage'], reverse=True) for i, sample_info in enumerate(samples, 1): print(f" {i:2d}. {sample_info['name']} ({sample_info['percentage']:.1f}%)") # 显示详细的段落信息 segments = sample_info['analysis']['motion_segments'] segment_summary = [] for seg in segments: if seg['duration'] >= 2: # 只显示持续时间>=2帧的段落 segment_summary.append(f"{seg['class']}({seg['duration']})") if segment_summary: print(f" 段落: {' -> '.join(segment_summary)}") else: print(" (无样本)") # 🔧 统计总体模式 print(f"\n" + "="*60) print("=== 总体统计 ===") total_forward = sum(a['class_distribution']['forward'] for a in all_analyses) total_backward = sum(a['class_distribution']['backward'] for a in all_analyses) total_left_turn = sum(a['class_distribution']['left_turn'] for a in all_analyses) total_right_turn = sum(a['class_distribution']['right_turn'] for a in all_analyses) total_frames = total_forward + total_backward + total_left_turn + total_right_turn print(f"总样本数: {len(all_analyses)}") print(f"总帧数: {total_frames}") print(f"Forward: {total_forward} 帧 ({total_forward/total_frames*100:.1f}%)") print(f"Backward: {total_backward} 帧 ({total_backward/total_frames*100:.1f}%)") print(f"Left Turn: {total_left_turn} 帧 ({total_left_turn/total_frames*100:.1f}%)") print(f"Right Turn: {total_right_turn} 帧 ({total_right_turn/total_frames*100:.1f}%)") # 🔧 样本分布统计 print(f"\n按主要类型的样本分布:") for class_name in ['forward', 'backward', 'left_turn', 'right_turn']: count = len(class_samples[class_name]) percentage = count / len(all_analyses) * 100 if all_analyses else 0 print(f" {class_name}: {count} 样本 ({percentage:.1f}%)") return all_analyses, class_samples def calculate_relative_rotation(current_rotation, reference_rotation): """计算相对旋转四元数""" q_current = torch.tensor(current_rotation, dtype=torch.float32) q_ref = torch.tensor(reference_rotation, dtype=torch.float32) # 计算参考旋转的逆 (q_ref^-1) q_ref_inv = torch.tensor([q_ref[0], -q_ref[1], -q_ref[2], -q_ref[3]]) # 四元数乘法计算相对旋转: q_relative = q_ref^-1 * q_current w1, x1, y1, z1 = q_ref_inv w2, x2, y2, z2 = q_current relative_rotation = torch.tensor([ w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2, w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2, w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2, w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2 ]) return relative_rotation if __name__ == "__main__": dataset_path = "/share_zhuyixuan05/zhuyixuan05/nuscenes_video_generation_2" print("开始详细分析pose分类（基于相对于reference的变化）...") all_analyses, class_samples = analyze_turning_patterns_detailed(dataset_path, num_samples=4000) print(f"\n🎉 分析完成! 共处理 {len(all_analyses)} 个样本")